Dynamic Assessment of Agriculture and Economic Growth Nexus in Morocco: Evidence from Structural VAR and Directed Acyclic Graphs
Keywords:
Agriculture, GDP, Structural VAR, Directed Acyclic GraphsAbstract
The recurrence of international crises and their negative impact on the economy and household food security has stimulated a strong revival of interest in the role of the agricultural sector and its relationship with the national economy. Recently, a macro-econometric model has shown a well-established bidirectional causality nexus between the agricultural sector and the Moroccan economy. However, the assessment of the magnitude of effects in both directions and their historical evolution are crucial topics that have not yet been explored. The current study empirically examines the dynamic interrelationships between Moroccan agriculture and GDP using the structural VAR model. The data set consists of the annual macroeconomic time series covering the period 1980-2019, namely: GDP per capita, agricultural GDP, investment rate, money supply and trade openness. This paper exploits recent advances in artificial intelligence to determine the over-identifying restrictions, through Directed Acyclic Graphs. Impulse response functions reveal that the Moroccan economy is very sensitive to agricultural shocks compared to shocks due to other endogenous variables, meanwhile the agricultural sector is very reactive to its shocks. The results from the variance decomposition show that the agricultural shocks are the most important driver of economic growth fluctuations and account for almost 69% of the forecast error variance for the first year. The share of GDP shocks in the variance of the forecast error of agricultural GDP does not exceed 7% for a ten-year horizon, while agricultural shocks dominate the decomposition variance profile and never fall below the 74% threshold. These results highlight the predominance of the Agriculture-Led Growth hypothesis in comparison with the Growth-Led Agriculture hypothesis. The findings resulting from the historical decomposition reconfirm the historical dependence between the national economy and agriculture. This sector sometimes acts as a shock absorber, counteracting the poor performance of other sectors of the economy. Under the Structural VAR model, the historical analysis illustrates that the national economy is increasingly resilient to agricultural shocks because of the improved resilience of Moroccan agriculture to climate shocks. Although the impact of agriculture is historically prominent, the magnitude of its impact has significantly reduced by 22% between 1982-1999 and 2000-2019. Given the strong potential of the agricultural sector to promote economic growth, policymakers should continue to create favorable conditions to support the development of the sector.
References
Diao, X., Hazell, P. and Thurlow, J., 2010. The Role of Agriculture in African Development. World Development, 38 (10): 1375-1383.
Ministry of Agriculture, Fisheries, Rural Development, Water and Forests (MAFRDWF), 2019. Agriculture en chiffres 2018.
Akesbi, N., 2013. L’agriculture marocaine, entre les contraintes de la dépendance alimentaire et les exigences de la régulation sociale. Critique économique, n°30.
Berrada, M., 2018. L’industrialisation, un impératif pour le développement. Hassan II Academy of Science and Technology.
Department of Economic Studies and Financial Forecast (DESFF), Ministry of Economy, Finance and Administration Reform, 2019. Le secteur agricole marocain: Tendances structurelles, enjeux et perspectives de développemen.
Ministry of Agriculture, Fisheries, Rural Development, Water and Forests (MAFRDWF), 2020. Le Maroc Vert 2008-2020.
Elalaoui, O., Fadlaoui, A., Maatala, N. and Ibrahimy, A., 2021. Agriculture and GDP Causality Nexus in Morocco: Empirical Evidence from a VAR Approach. International Journal of Agricultural Economics, 6 (4): 198-207.
Raghavan, M., Silvapulle, P. and Athanasopoulos, G., 2011. Structural VAR models for Malaysian monetary policy analysis during the pre- and post-1997 Asian crisis periods. Applied Economics, 44 (29): 3841-3856.
Samimi, A. J., Asadi, S. P. and Sheidaei, Z., 2018. The international spillover of china’s monetary policy: a case study of a developing country. China Economic Journal, 12 (01): 3841-3856
Neusser, K., 2016. Time Series Econometrics. Springer Texts in Business and Economics.
Adenomon, M. O. and Oyejola, B. A., 2013. Impact of Agriculture and Industrialization on GDP in Nigeria: Evidence from VAR and SVAR Models. International Journal of Analysis and Application, 1 (1): 40-78.
Adewole, A. I., Bodunwa, O. K. and Akinyanju, M. M., 2020. Structural vector autoregressive modeling of some factors that affect the economic growth in Nigeria. Science World Journal, 15 (2).
Mai, X., Chan, R. C. K. and Zhan, C., 2019. Which Sectors Really Matter for a Resilient Chinese Economy? A Structural Decomposition Analysis. Sustainability, 11 (22).
Yetiz, F. and Özden, C., 2017. Analysis of causal relationship among GDP, agricultural, industrial and services sector growth in Turkey. Ömer Halisdemir Üniversitesi, İktisadi ve İdari Bilimler Fakültesi Dergisi, 10 (3): 75-84.
Darolles, S. and Gourieroux, C., 2015. Contagion in Structural VARMA Models. Contagion Phenomena with Applications in Finance, 19–44.
Awokuse, T. O. and Xie, R., 2014. Does Agriculture Really Matter for Economic Growth in Developing Countries?. Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie. 63 (1): 77-99.
Kožić, Y., 2014. Detecting international tourism demand growth cycles. Current Issues in Tourism, 17 (5): 309–403.
Asghar, Z. and Rahat, T., 2011, Energy-Gdp Causal Relationship For Pakistan: A Graph Theoretic Approach. Applied Econometrics and International Development, 11 (1).
Fazal, R., Rehman, S. A. U., Rehman, A. U., Bhatti, M. I. and Hussain, A., 2021. Energy-environment-economy causal nexus in Pakistan: A graph theoretic approach. Energy, 214
Ji, Q., Bouri, E., Gupta, R. and Roubaud, D., 2018. Network causality structures among Bitcoin and other financial assets: A directed acyclic graph approach. The Quarterly Review of Economics and Finance, 70: 203-213.
Ji, Q., Zhang, H. Y. and Geng, J. B., 2017. What drives natural gas prices in the United States? – A directed acyclic graph approach. Energy economics, 69: 79-88
Li, Y., Woodard, J. D. and Leatham, D. J., 2013. Causality among Foreign Direct Investment and Economic Growth: A Directed Acyclic Graph Approach. Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, 45 (4): 1-20
Miljkovic, D. and Goetz, C., 2020. The effects of futures markets on oil spot price volatility in regional US markets. Applied Energy, 273.
Wang, R., Qi, Z. and Shu, Y., 2020. Multiple relationships between fixed-asset investment and industrial structure evolution in China–Based on Directed Acyclic Graph (DAG) analysis and VAR model. Structural Change and Economic Dynamics, 55: 222-231.
Yang, Z., and Zhao, Y., 2014. Energy consumption, carbon emissions, and economic growth in India: Evidence from directed acyclic graphs. Economic Modelling, 38: 533–540
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Ouahiba Elalaoui, Abdelouafi Ibrahimy, Aziz Fadlaou
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.